app.py

import gradio as gr

from transformers import set_seed
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM

import torch
import random
import numpy as np

seed = 42
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
set_seed(seed)

tokenizer = AutoTokenizer.from_pretrained("Oysiyl/elvish-translator-quenya-t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("Oysiyl/elvish-translator-quenya-t5-base")

prefix = "translate English to Elvish: "

def predict(text):
    inputs = tokenizer(prefix + text, return_tensors="pt").input_ids
    outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return result

demo = gr.Interface(title="English to Elvish translation!", 
                    description="<p style='text-align: center'>Provide English text and let's model try to guess the text in Elvish!</p>", 
                    article = "<p style='text-align: center'>Text Translation English -> Elvish | Demo Model</p>",
                    fn=predict, inputs="text", outputs="text")
demo.launch()